Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification

TL;DR

This paper introduces a localized multiple kernel learning method for one-class classification, enhancing anomaly detection by adaptively assigning kernel weights locally, leading to improved performance over existing methods.

Contribution

It proposes a novel LMKAD approach that assigns local kernel weights via a gating function, optimizing parameters through a two-step process, and demonstrates superior results on benchmark datasets.

Findings

Achieves higher Gmean scores than existing MKAD methods.

Uses fewer support vectors for comparable or better performance.

Statistically significant improvements confirmed by Friedman test.

Abstract

Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. Our algorithm achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.